Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations932
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory94.8 KiB
Average record size in memory104.1 B

Variable types

Text2
Categorical3
Numeric7
DateTime1

Alerts

ItemType SalesChannel is highly overall correlated with Total and 1 other fieldsHigh correlation
Revenue is highly overall correlated with Total and 4 other fieldsHigh correlation
Total is highly overall correlated with ItemType SalesChannel and 4 other fieldsHigh correlation
TotalCost is highly overall correlated with Revenue and 4 other fieldsHigh correlation
TotalProfit is highly overall correlated with Revenue and 4 other fieldsHigh correlation
UnitCost is highly overall correlated with ItemType SalesChannel and 4 other fieldsHigh correlation
UnitPrice is highly overall correlated with Revenue and 2 other fieldsHigh correlation
UnitsSold has unique valuesUnique

Reproduction

Analysis started2024-08-12 17:10:28.824616
Analysis finished2024-08-12 17:10:43.447645
Duration14.62 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Region
Text

Distinct112
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:43.757385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length51
Median length48
Mean length17.56867
Min length4

Characters and Unicode

Total characters16374
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)4.1%

Sample

1st rowMiddle East and North Africa
2nd rowNorth America
3rd rowMiddle East and North Africa
4th rowAsia
5th rowSub-Saharan Africa
ValueCountFrequency (%)
africa 374
16.1%
and 294
 
12.6%
north 135
 
5.8%
east 131
 
5.6%
america 107
 
4.6%
caribbean 92
 
3.9%
the 92
 
3.9%
central 92
 
3.9%
oceania 71
 
3.0%
0europe 30
 
1.3%
Other values (79) 912
39.1%
2024-08-12T17:10:44.724605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2332
14.2%
1914
 
11.7%
r 1387
 
8.5%
i 988
 
6.0%
e 858
 
5.2%
n 806
 
4.9%
A 682
 
4.2%
u 564
 
3.4%
c 563
 
3.4%
d 558
 
3.4%
Other values (31) 5722
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2332
14.2%
1914
 
11.7%
r 1387
 
8.5%
i 988
 
6.0%
e 858
 
5.2%
n 806
 
4.9%
A 682
 
4.2%
u 564
 
3.4%
c 563
 
3.4%
d 558
 
3.4%
Other values (31) 5722
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2332
14.2%
1914
 
11.7%
r 1387
 
8.5%
i 988
 
6.0%
e 858
 
5.2%
n 806
 
4.9%
A 682
 
4.2%
u 564
 
3.4%
c 563
 
3.4%
d 558
 
3.4%
Other values (31) 5722
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2332
14.2%
1914
 
11.7%
r 1387
 
8.5%
i 988
 
6.0%
e 858
 
5.2%
n 806
 
4.9%
A 682
 
4.2%
u 564
 
3.4%
c 563
 
3.4%
d 558
 
3.4%
Other values (31) 5722
34.9%
Distinct190
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:45.287058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length27
Mean length8.5321888
Min length4

Characters and Unicode

Total characters7952
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.2%

Sample

1st rowLibya
2nd rowCanada
3rd rowLibya
4th rowJapan
5th rowChad
ValueCountFrequency (%)
republic 31
 
2.6%
and 23
 
1.9%
the 22
 
1.8%
of 19
 
1.6%
south 16
 
1.3%
sudan 15
 
1.3%
islands 14
 
1.2%
saint 12
 
1.0%
guinea 11
 
0.9%
congo 11
 
0.9%
Other values (207) 1025
85.5%
2024-08-12T17:10:46.222091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1206
15.2%
i 685
 
8.6%
n 623
 
7.8%
e 532
 
6.7%
o 413
 
5.2%
r 403
 
5.1%
l 316
 
4.0%
u 306
 
3.8%
t 299
 
3.8%
270
 
3.4%
Other values (43) 2899
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1206
15.2%
i 685
 
8.6%
n 623
 
7.8%
e 532
 
6.7%
o 413
 
5.2%
r 403
 
5.1%
l 316
 
4.0%
u 306
 
3.8%
t 299
 
3.8%
270
 
3.4%
Other values (43) 2899
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1206
15.2%
i 685
 
8.6%
n 623
 
7.8%
e 532
 
6.7%
o 413
 
5.2%
r 403
 
5.1%
l 316
 
4.0%
u 306
 
3.8%
t 299
 
3.8%
270
 
3.4%
Other values (43) 2899
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1206
15.2%
i 685
 
8.6%
n 623
 
7.8%
e 532
 
6.7%
o 413
 
5.2%
r 403
 
5.1%
l 316
 
4.0%
u 306
 
3.8%
t 299
 
3.8%
270
 
3.4%
Other values (43) 2899
36.5%

ItemType SalesChannel
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
Beverages
97 
Vegetables
94 
Office Supplies
84 
Personal Care
83 
Baby Food
79 
Other values (7)
495 

Length

Max length15
Median length13
Mean length8.7854077
Min length4

Characters and Unicode

Total characters8188
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCosmetics
2nd rowVegetables
3rd rowBaby Food
4th rowCereal
5th rowFruits

Common Values

ValueCountFrequency (%)
Beverages 97
10.4%
Vegetables 94
10.1%
Office Supplies 84
9.0%
Personal Care 83
8.9%
Baby Food 79
8.5%
Snacks 74
7.9%
Household 73
7.8%
Clothes 71
7.6%
Meat 71
7.6%
Cereal 70
7.5%
Other values (2) 136
14.6%

Length

2024-08-12T17:10:46.705128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beverages 97
 
8.2%
vegetables 94
 
8.0%
office 84
 
7.1%
supplies 84
 
7.1%
personal 83
 
7.0%
care 83
 
7.0%
baby 79
 
6.7%
food 79
 
6.7%
snacks 74
 
6.3%
household 73
 
6.2%
Other values (5) 348
29.5%

Most occurring characters

ValueCountFrequency (%)
e 1331
16.3%
s 781
 
9.5%
a 651
 
8.0%
o 527
 
6.4%
l 475
 
5.8%
r 400
 
4.9%
t 372
 
4.5%
i 304
 
3.7%
C 293
 
3.6%
246
 
3.0%
Other values (21) 2808
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1331
16.3%
s 781
 
9.5%
a 651
 
8.0%
o 527
 
6.4%
l 475
 
5.8%
r 400
 
4.9%
t 372
 
4.5%
i 304
 
3.7%
C 293
 
3.6%
246
 
3.0%
Other values (21) 2808
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1331
16.3%
s 781
 
9.5%
a 651
 
8.0%
o 527
 
6.4%
l 475
 
5.8%
r 400
 
4.9%
t 372
 
4.5%
i 304
 
3.7%
C 293
 
3.6%
246
 
3.0%
Other values (21) 2808
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1331
16.3%
s 781
 
9.5%
a 651
 
8.0%
o 527
 
6.4%
l 475
 
5.8%
r 400
 
4.9%
t 372
 
4.5%
i 304
 
3.7%
C 293
 
3.6%
246
 
3.0%
Other values (21) 2808
34.3%

OrderPriority
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
Offline
483 
Online
449 

Length

Max length7
Median length7
Mean length6.5182403
Min length6

Characters and Unicode

Total characters6075
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline
2nd rowOnline
3rd rowOffline
4th rowOffline
5th rowOffline

Common Values

ValueCountFrequency (%)
Offline 483
51.8%
Online 449
48.2%

Length

2024-08-12T17:10:47.334588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T17:10:47.553281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
offline 483
51.8%
online 449
48.2%

Most occurring characters

ValueCountFrequency (%)
n 1381
22.7%
f 966
15.9%
O 932
15.3%
l 932
15.3%
i 932
15.3%
e 932
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1381
22.7%
f 966
15.9%
O 932
15.3%
l 932
15.3%
i 932
15.3%
e 932
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1381
22.7%
f 966
15.9%
O 932
15.3%
l 932
15.3%
i 932
15.3%
e 932
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1381
22.7%
f 966
15.9%
O 932
15.3%
l 932
15.3%
i 932
15.3%
e 932
15.3%

OrderID
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
L
249 
C
247 
M
229 
H
207 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters932
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowC
4th rowC
5th rowH

Common Values

ValueCountFrequency (%)
L 249
26.7%
C 247
26.5%
M 229
24.6%
H 207
22.2%

Length

2024-08-12T17:10:47.731390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T17:10:47.951631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
l 249
26.7%
c 247
26.5%
m 229
24.6%
h 207
22.2%

Most occurring characters

ValueCountFrequency (%)
L 249
26.7%
C 247
26.5%
M 229
24.6%
H 207
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 249
26.7%
C 247
26.5%
M 229
24.6%
H 207
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 249
26.7%
C 247
26.5%
M 229
24.6%
H 207
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 249
26.7%
C 247
26.5%
M 229
24.6%
H 207
22.2%

UnitsSold
Real number (ℝ)

UNIQUE 

Distinct932
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4880586 × 108
Minimum1.0292801 × 108
Maximum9.9552983 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:48.188894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0292801 × 108
5-th percentile1.4669453 × 108
Q13.2736753 × 108
median5.5410002 × 108
Q37.6879841 × 108
95-th percentile9.4942607 × 108
Maximum9.9552983 × 108
Range8.9260182 × 108
Interquartile range (IQR)4.4143088 × 108

Descriptive statistics

Standard deviation2.5620493 × 108
Coefficient of variation (CV)0.46684074
Kurtosis-1.1827043
Mean5.4880586 × 108
Median Absolute Deviation (MAD)2.2089751 × 108
Skewness-0.022968805
Sum5.1148706 × 1011
Variance6.5640968 × 1016
MonotonicityNot monotonic
2024-08-12T17:10:48.456789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
686800706 1
 
0.1%
410231912 1
 
0.1%
798784863 1
 
0.1%
985092818 1
 
0.1%
325412309 1
 
0.1%
447917163 1
 
0.1%
903740775 1
 
0.1%
794969689 1
 
0.1%
584204280 1
 
0.1%
901180875 1
 
0.1%
Other values (922) 922
98.9%
ValueCountFrequency (%)
102928006 1
0.1%
103617227 1
0.1%
104845464 1
0.1%
105117976 1
0.1%
105390059 1
0.1%
105558288 1
0.1%
105966842 1
0.1%
106578814 1
0.1%
106753051 1
0.1%
107005393 1
0.1%
ValueCountFrequency (%)
995529830 1
0.1%
994566810 1
0.1%
991644704 1
0.1%
990708720 1
0.1%
989928519 1
0.1%
989119565 1
0.1%
987714517 1
0.1%
986442506 1
0.1%
985665738 1
0.1%
985092818 1
0.1%

UnitPrice
Real number (ℝ)

HIGH CORRELATION 

Distinct896
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5055.0558
Minimum13
Maximum9998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:48.742301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile445.4
Q12420.25
median5202.5
Q37587.25
95-th percentile9529.8
Maximum9998
Range9985
Interquartile range (IQR)5167

Descriptive statistics

Standard deviation2917.1905
Coefficient of variation (CV)0.57708373
Kurtosis-1.2341792
Mean5055.0558
Median Absolute Deviation (MAD)2568
Skewness-0.053698272
Sum4711312
Variance8510000.2
MonotonicityNot monotonic
2024-08-12T17:10:49.014677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6283 3
 
0.3%
8128 2
 
0.2%
8775 2
 
0.2%
7913 2
 
0.2%
1578 2
 
0.2%
2352 2
 
0.2%
1547 2
 
0.2%
9669 2
 
0.2%
4247 2
 
0.2%
822 2
 
0.2%
Other values (886) 911
97.7%
ValueCountFrequency (%)
13 1
0.1%
25 1
0.1%
33 1
0.1%
61 1
0.1%
64 1
0.1%
70 1
0.1%
80 1
0.1%
89 1
0.1%
103 1
0.1%
114 1
0.1%
ValueCountFrequency (%)
9998 1
0.1%
9980 1
0.1%
9958 1
0.1%
9951 1
0.1%
9950 1
0.1%
9942 1
0.1%
9930 1
0.1%
9929 1
0.1%
9928 1
0.1%
9924 1
0.1%

UnitCost
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.49705
Minimum9.33
Maximum668.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:49.240610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.33
5-th percentile9.33
Q181.73
median154.06
Q3421.89
95-th percentile668.27
Maximum668.27
Range658.94
Interquartile range (IQR)340.16

Descriptive statistics

Standard deviation217.41799
Coefficient of variation (CV)0.83143573
Kurtosis-0.74942399
Mean261.49705
Median Absolute Deviation (MAD)101.22
Skewness0.79283801
Sum243715.25
Variance47270.582
MonotonicityNot monotonic
2024-08-12T17:10:49.434466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
47.45 97
10.4%
154.06 94
10.1%
651.21 84
9.0%
81.73 83
8.9%
255.28 79
8.5%
152.58 74
7.9%
668.27 73
7.8%
109.28 71
7.6%
421.89 71
7.6%
205.7 70
7.5%
Other values (2) 136
14.6%
ValueCountFrequency (%)
9.33 67
7.2%
47.45 97
10.4%
81.73 83
8.9%
109.28 71
7.6%
152.58 74
7.9%
154.06 94
10.1%
205.7 70
7.5%
255.28 79
8.5%
421.89 71
7.6%
437.2 69
7.4%
ValueCountFrequency (%)
668.27 73
7.8%
651.21 84
9.0%
437.2 69
7.4%
421.89 71
7.6%
255.28 79
8.5%
205.7 70
7.5%
154.06 94
10.1%
152.58 74
7.9%
109.28 71
7.6%
81.73 83
8.9%

Total
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.75343
Minimum6.92
Maximum524.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:49.642981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.92
5-th percentile6.92
Q135.84
median97.44
Q3263.33
95-th percentile524.96
Maximum524.96
Range518.04
Interquartile range (IQR)227.49

Descriptive statistics

Standard deviation176.20627
Coefficient of variation (CV)0.95373747
Kurtosis-0.61295248
Mean184.75343
Median Absolute Deviation (MAD)61.98
Skewness0.94731478
Sum172190.2
Variance31048.651
MonotonicityNot monotonic
2024-08-12T17:10:49.847138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
31.79 97
10.4%
90.93 94
10.1%
524.96 84
9.0%
56.67 83
8.9%
159.42 79
8.5%
97.44 74
7.9%
502.54 73
7.8%
35.84 71
7.6%
364.69 71
7.6%
117.11 70
7.5%
Other values (2) 136
14.6%
ValueCountFrequency (%)
6.92 67
7.2%
31.79 97
10.4%
35.84 71
7.6%
56.67 83
8.9%
90.93 94
10.1%
97.44 74
7.9%
117.11 70
7.5%
159.42 79
8.5%
263.33 69
7.4%
364.69 71
7.6%
ValueCountFrequency (%)
524.96 84
9.0%
502.54 73
7.8%
364.69 71
7.6%
263.33 69
7.4%
159.42 79
8.5%
117.11 70
7.5%
97.44 74
7.9%
90.93 94
10.1%
56.67 83
8.9%
35.84 71
7.6%

Revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct931
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1326361.3
Minimum2043.25
Maximum6617209.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:50.091601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2043.25
5-th percentile36638.91
Q1266243.72
median739383.6
Q31768543.1
95-th percentile4750751.3
Maximum6617209.5
Range6615166.3
Interquartile range (IQR)1502299.3

Descriptive statistics

Standard deviation1491698.5
Coefficient of variation (CV)1.1246548
Kurtosis1.9586406
Mean1326361.3
Median Absolute Deviation (MAD)588048.01
Skewness1.6097719
Sum1.2361687 × 109
Variance2.2251644 × 1012
MonotonicityNot monotonic
2024-08-12T17:10:50.361026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90211.77 2
 
0.2%
3692591.2 1
 
0.1%
3738302.38 1
 
0.1%
460786.95 1
 
0.1%
852617.04 1
 
0.1%
355732.65 1
 
0.1%
54421.89 1
 
0.1%
3397058.28 1
 
0.1%
861563.52 1
 
0.1%
1707721.4 1
 
0.1%
Other values (921) 921
98.8%
ValueCountFrequency (%)
2043.25 1
0.1%
2061.93 1
0.1%
2556.42 1
0.1%
2733.69 1
0.1%
3592.05 1
0.1%
4114.53 1
0.1%
5409.3 1
0.1%
7273.97 1
0.1%
7501.32 1
0.1%
7669.26 1
0.1%
ValueCountFrequency (%)
6617209.54 1
0.1%
6557065.24 1
0.1%
6456747.15 1
0.1%
6424186.65 1
0.1%
6354579.43 1
0.1%
6306968.85 1
0.1%
6263026.44 1
0.1%
6216247.54 1
0.1%
6209287.35 1
0.1%
6207333.72 1
0.1%

TotalCost
Real number (ℝ)

HIGH CORRELATION 

Distinct931
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean937575.8
Minimum1416.75
Maximum5204978.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:50.635460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1416.75
5-th percentile23194.373
Q1160430.65
median456653.72
Q31151713
95-th percentile3696940.8
Maximum5204978.4
Range5203561.7
Interquartile range (IQR)991282.37

Descriptive statistics

Standard deviation1169022.2
Coefficient of variation (CV)1.2468562
Kurtosis2.4612689
Mean937575.8
Median Absolute Deviation (MAD)372660.5
Skewness1.7810015
Sum8.7382065 × 108
Variance1.3666129 × 1012
MonotonicityNot monotonic
2024-08-12T17:10:50.951238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66909.48 2
 
0.2%
2224085.18 1
 
0.1%
2811208.76 1
 
0.1%
308712.69 1
 
0.1%
544494.72 1
 
0.1%
238329.63 1
 
0.1%
40364.36 1
 
0.1%
2936483.88 1
 
0.1%
282562.56 1
 
0.1%
972247.22 1
 
0.1%
Other values (921) 921
98.8%
ValueCountFrequency (%)
1416.75 1
0.1%
1529.32 1
0.1%
1896.08 1
0.1%
2027.56 1
0.1%
2664.2 1
0.1%
3051.72 1
0.1%
3624.06 1
0.1%
4193.28 1
0.1%
5043.63 1
0.1%
5232.64 1
0.1%
ValueCountFrequency (%)
5204978.4 1
0.1%
5178730.4 1
0.1%
5084237.6 1
0.1%
5005493.6 1
0.1%
5003918.72 1
0.1%
4976151.08 1
0.1%
4930922.48 1
0.1%
4885277.76 1
0.1%
4854305.12 1
0.1%
4778652.86 1
0.1%

TotalProfit
Real number (ℝ)

HIGH CORRELATION 

Distinct931
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388785.47
Minimum532.61
Maximum1726181.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 KiB
2024-08-12T17:10:51.214522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum532.61
5-th percentile10260.918
Q194933.545
median272935.17
Q3550521.42
95-th percentile1235843.6
Maximum1726181.4
Range1725648.8
Interquartile range (IQR)455587.87

Descriptive statistics

Standard deviation381946.21
Coefficient of variation (CV)0.98240865
Kurtosis1.4517228
Mean388785.47
Median Absolute Deviation (MAD)207071.82
Skewness1.3680055
Sum3.6234805 × 108
Variance1.458829 × 1011
MonotonicityNot monotonic
2024-08-12T17:10:51.490853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23302.29 2
 
0.2%
1468506.02 1
 
0.1%
927093.62 1
 
0.1%
152074.26 1
 
0.1%
308122.32 1
 
0.1%
117403.02 1
 
0.1%
14057.53 1
 
0.1%
460574.4 1
 
0.1%
579000.96 1
 
0.1%
735474.18 1
 
0.1%
Other values (921) 921
98.8%
ValueCountFrequency (%)
532.61 1
0.1%
626.5 1
0.1%
660.34 1
0.1%
706.13 1
0.1%
927.85 1
0.1%
1062.81 1
0.1%
1785.24 1
0.1%
1937.64 1
0.1%
1981.02 1
0.1%
2154.49 1
0.1%
ValueCountFrequency (%)
1726181.36 1
0.1%
1725485.88 1
0.1%
1697666.68 1
0.1%
1682887.73 1
0.1%
1641058.46 1
0.1%
1631422.21 1
0.1%
1626142.76 1
0.1%
1587954.71 1
0.1%
1575926.57 1
0.1%
1571089.32 1
0.1%
Distinct317
Distinct (%)34.2%
Missing4
Missing (%)0.4%
Memory size7.4 KiB
Minimum2022-01-11 00:00:00
Maximum2023-04-30 00:00:00
2024-08-12T17:10:51.776071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:52.048510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-08-12T17:10:40.921226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:29.360712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:31.512758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:33.934427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:35.645847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:37.301984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:39.195269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:41.156473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:29.605704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:31.856553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:34.157915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:35.879952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:37.739075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:39.467317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:41.402150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:29.846020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:32.178414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:34.420993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:36.118507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:37.966410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:39.709113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:41.683648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:30.069955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:32.555371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:34.660004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:36.360970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:38.211590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:39.953060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:41.915855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:30.545116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:32.922045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:34.884761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:36.593639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:38.477816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:40.187061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:42.174715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:30.812055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:33.308073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:35.123814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:36.826581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:38.712453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:40.437912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:42.428610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:31.139233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:33.666889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:35.398172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:37.054787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:38.948446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T17:10:40.675849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-12T17:10:52.276225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
UnitsSoldItemType SalesChannelOrderIDOrderPriorityRevenueTotalTotalCostTotalProfitUnitCostUnitPrice
UnitsSold1.0000.0260.0000.076-0.014-0.039-0.0210.000-0.0280.008
ItemType SalesChannel0.0261.0000.0000.1030.3450.9970.3540.3360.9970.000
OrderID0.0000.0001.0000.0290.0000.0000.0270.0070.0340.066
OrderPriority0.0760.1030.0291.0000.0400.0710.0580.0590.0800.056
Revenue-0.0140.3450.0000.0401.0000.7470.9880.9460.7530.584
Total-0.0390.9970.0000.0710.7471.0000.7830.6100.9720.015
TotalCost-0.0210.3540.0270.0580.9880.7831.0000.8920.7690.554
TotalProfit0.0000.3360.0070.0590.9460.6100.8921.0000.6590.622
UnitCost-0.0280.9970.0340.0800.7530.9720.7690.6591.0000.010
UnitPrice0.0080.0000.0660.0560.5840.0150.5540.6220.0101.000

Missing values

2024-08-12T17:10:42.795208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T17:10:43.230513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RegionCountryItemType SalesChannelOrderPriorityOrderIDUnitsSoldUnitPriceUnitCostTotalRevenueTotalCostTotalProfitorder_date
0Middle East and North AfricaLibyaCosmeticsOfflineM6868007068446437.20263.333692591.202224085.181468506.022023-02-13
1North AmericaCanadaVegetablesOnlineM1859413023018154.0690.93464953.08274426.74190526.342023-03-28
2Middle East and North AfricaLibyaBaby FoodOfflineC2462223411517255.28159.42387259.76241840.14145419.622022-09-08
3AsiaJapanCerealOfflineC1614426493322205.70117.11683335.40389039.42294295.982023-01-07
4Sub-Saharan AfricaChadFruitsOfflineH64571355598459.336.9291853.8568127.4023726.452022-04-23
5EuropeArmeniaCerealOnlineH6834588889528205.70117.111959909.601115824.08844085.522022-06-20
6Sub-Saharan AfricaEritreaCerealOnlineH6794149752844205.70117.11585010.80333060.84251949.962022-08-09
7EuropeMontenegroClothesOfflineM2086306457299109.2835.84797634.72261596.16536038.562023-04-06
8Central America and the CaribbeanJamaicaVegetablesOnlineH2664672252428154.0690.93374057.68220778.04153279.642022-06-05
9Australia and OceaniaFijiVegetablesOfflineH1185985444800154.0690.93739488.00436464.00303024.002023-02-13
RegionCountryItemType SalesChannelOrderPriorityOrderIDUnitsSoldUnitPriceUnitCostTotalRevenueTotalCostTotalProfitorder_date
9229Australia and OceaniaFijiCerealOnlineL9032781488932205.70117.111837312.401046026.52791285.882023-02-08
9230Sub-Saharan AfricaMaliBeveragesOnlineL41045249787047.4531.7941281.5027657.3013624.202023-04-20
9241Sub-Saharan AfricaLiberiaCerealOfflineH6426833033126205.70117.11643018.20366085.86276932.342022-06-07
9252EuropeSwitzerlandBeveragesOfflineL682831895398747.4531.79189183.15126746.7362436.422022-10-12
9263Australia and OceaniaSamoaBaby FoodOnlineL5840721018769255.28159.422238550.321397953.98840596.342022-05-12
9274AsiaNepalMeatOfflineC9198902484821421.89364.692033931.691758170.49275761.202022-02-21
9285Middle East and North AfricaAzerbaijanSnacksOfflineC5340851666524152.5897.44995431.92635698.56359733.362022-11-06
9296EuropeGeorgiaBaby FoodOfflineH590768182288255.28159.4273520.6445912.9627607.682022-10-07
9307Middle East and North AfricaUnited Arab EmiratesVegetablesOnlineC5243631249556154.0690.931472197.36868927.08603270.282022-08-20
9319EuropePortugalCerealOfflineC8115465993528205.70117.11725709.60413164.08312545.522022-06-30